Some other covid19 visualizations:

https://coronavirus.1point3acres.com/

https://coronavirus.jhu.edu/map.html

# data source https://www.census.gov/data/datasets/time-series/demo/popest/2010s-state-total.html and wikipedia
df_population <- data.frame(
  state = c("AK", "AL", "AR", "AS", "AZ", "CA", "CO", "CT", "DC", "DE", "FL", 
            "GA", "GU", "HI", "IA", "ID", "IL", "IN", "KS", "KY", "LA", "MA", 
            "MD", "ME", "MI", "MN", "MO", "MP", "MS", "MT", "NC", "ND", "NE", 
            "NH", "NJ", "NM", "NV", "NY", "OH", "OK", "OR", "PA", "PR", "RI", 
            "SC", "SD", "TN", "TX", "UT", "VA", "VI", "VT", "WA", "WI", "WV", "WY"),
  population = c(731545, 4903185, 3017804, 55465 , 7278717, 39512223, 5758736, 3565287, 705749, 973764, 21477737,
                 10617423, 165768, 1415872, 3155070, 1787065, 12671821, 6732219, 2913314, 4467673, 4648794, 6892503, 
                 6045680, 1344212,  9986857, 5639632, 6137428, 56882, 2976149, 1068778, 10488084, 762062, 1934408,
                 1359711, 8882190, 2096829, 3080156, 19453561, 11689100, 3956971, 4217737, 12801989, 3193694, 1059361,
                 5148714, 884659, 6829174, 28995881, 3205958, 8535519, 106977 , 623989, 7614893, 5822434, 1792147, 578759)
)

# The Atlantic Monthly Group (CC BY-NC 4.0)
# source: https://covidtracking.com/api

df_states <- fread("https://covidtracking.com/api/v1/states/daily.csv") %>% 
               replace(is.na(.), 0) %>%
               inner_join(df_population, by = "state")%>%
               mutate(date = as.Date(as.character(date), "%Y%m%d"))

tableau10 <- as.list(ggthemes_data[["tableau"]][["color-palettes"]][["regular"]][[1]][,2])$value
first_day <- as.Date("2020-03-15") # to select a date
today <-  as.Date(toString(max(df_states$date)))
  
kable(head(df_states, n = 3))
date state positive negative pending hospitalizedCurrently hospitalizedCumulative inIcuCurrently inIcuCumulative onVentilatorCurrently onVentilatorCumulative recovered dataQualityGrade lastUpdateEt dateModified checkTimeEt death hospitalized dateChecked totalTestsViral positiveTestsViral negativeTestsViral positiveCasesViral deathConfirmed deathProbable totalTestEncountersViral totalTestsPeopleViral totalTestsAntibody positiveTestsAntibody negativeTestsAntibody totalTestsPeopleAntibody positiveTestsPeopleAntibody negativeTestsPeopleAntibody totalTestsPeopleAntigen positiveTestsPeopleAntigen totalTestsAntigen positiveTestsAntigen fips positiveIncrease negativeIncrease total totalTestResultsSource totalTestResults totalTestResultsIncrease posNeg deathIncrease hospitalizedIncrease hash commercialScore negativeRegularScore negativeScore positiveScore score grade population
2020-09-14 AK 7270 396143 0 37 0 0 0 8 0 2377 A 9/14/2020 03:59 2020-09-14T03:59:00Z 09/13 23:59 44 0 2020-09-14T03:59:00Z 403413 6533 396600 7270 44 0 0 0 0 0 0 0 0 0 0 0 0 0 2 77 2123 403413 posNeg 403413 2200 403413 0 0 c47ba6fdd7b2d5856a69b861c773b64ba0dfcb76 0 0 0 0 0 0 731545
2020-09-14 AL 139459 888092 0 792 15756 0 1611 0 892 54223 B 9/14/2020 11:00 2020-09-14T11:00:00Z 09/14 07:00 2355 15756 2020-09-14T11:00:00Z 1014391 0 0 126299 2221 134 0 0 0 0 0 55780 0 0 0 0 0 0 1 704 3660 1027551 posNeg 1027551 4364 1027551 4 229 a852a907f21a1d2ec711d522217d2fa536b912e5 0 0 0 0 0 0 4903185
2020-09-14 AR 70627 757640 0 378 4736 175 0 76 603 62740 A+ 9/14/2020 00:00 2020-09-14T00:00:00Z 09/13 20:00 992 4736 2020-09-14T00:00:00Z 827089 0 757640 69449 986 6 0 0 0 0 0 0 0 0 0 0 18516 2779 5 408 5791 828267 posNeg 828267 6199 828267 11 17 ca2f4e7d88ac2334155d572a28bde6974977f350 0 0 0 0 0 0 3017804

Rhode Island (as I live in RI now)

df_states %>% filter(state == "RI") %>%
    ggplot() + 
      geom_label(x = first_day, y = 650, color = "darkgray", label = "total positive", size = 2, hjust = 0) + 
      geom_text(mapping = aes(x = date, y = 600, label = positive), color = "darkgray", size = 2, angle = 90, hjust = 0)+ 
      #geom_label(x = first_day, y = 800, color = "black", label = "death", size = 2, hjust = 0) + 
      geom_label(x = first_day, y = 550, color = tableau10[2], label = "positiveIncrease", size = 2, hjust = 0) + 
      geom_label(x = first_day, y = 500, color = tableau10[1], label = "hospitalizedCurrently", size = 2, hjust = 0) + 
      # geom_line(mapping = aes(x = date, y = death), alpha = 0.7, color = "black", size = LINE_SIZE) + 
      # geom_text(mapping = aes(x = date - 0.5, y = death + 10, label = death), color = "black", size = 1.5) + 
      # geom_point(mapping = aes(x = date, y = death), color = "black", shape = 10) + 
      geom_line(mapping = aes(x = date, y = hospitalizedCurrently), alpha = 0.7, color = tableau10[1], size = LINE_SIZE) + 
      geom_text(mapping = aes(x = date - 0.5, y = hospitalizedCurrently + 10, label = hospitalizedCurrently), color =  tableau10[1], size = 1.5) + 
      geom_point(mapping = aes(x = date, y = hospitalizedCurrently), color = tableau10[1], shape = 15) + 
      geom_line(mapping = aes(x = date, y = positiveIncrease), alpha = 0.7, color = tableau10[2], size = LINE_SIZE) + 
      geom_text(mapping = aes(x = date - 0.5, y = positiveIncrease + 10, label = positiveIncrease), color =  tableau10[2], size = 1.5)+ 
      geom_point(mapping = aes(x = date, y = positiveIncrease), color = tableau10[2]) + 
      scale_x_date(limits = c(first_day, today), breaks = seq(first_day, today, by = "day")) + 
      xlab("Date") + ylab("") + ggtitle("RI")

US - all states

df_states %>% group_by(date) %>%
    summarise(positiveIncrease = sum(positiveIncrease), hospitalizedCurrently = sum(hospitalizedCurrently), total = sum(positive)) %>% 
    ungroup() %>%
    ggplot() + 
     geom_label(x = first_day, y = 68000, color = "darkgray", label = "total positive: ", size = 2, hjust = 0) +
     geom_text(mapping = aes(x = date, y = 70000, label = total), color = "darkgray", size = 2, angle = 90, hjust = 0) +
     geom_label(x = first_day, y = 50000, color = tableau10[1], label = "hospitalizedCurrently", size = 2, hjust = 0) +
     geom_label(x = first_day, y = 55000, color = tableau10[2], label = "positiveIncrease", size = 2, hjust = 0) +
     geom_line(mapping = aes(x = date, y = hospitalizedCurrently), alpha = 0.7, color = tableau10[1], size = LINE_SIZE) +
     geom_text(mapping = aes(x = date - 0.5, y = hospitalizedCurrently + 1000, label = hospitalizedCurrently), color =  tableau10[1], size = 1.5) +
     geom_point(mapping = aes(x = date, y = hospitalizedCurrently), color = tableau10[1], shape = 15) +
     geom_line(mapping = aes(x = date, y = positiveIncrease), alpha = 0.7, color = tableau10[2], size = LINE_SIZE) +
     geom_text(mapping = aes(x = date - 0.5, y = positiveIncrease + 1000, label = positiveIncrease), color =  tableau10[2], size = 1.5) +
     geom_point(mapping = aes(x = date, y = positiveIncrease), color = tableau10[2]) +
     scale_x_date(limits = c(first_day, today), breaks = seq(first_day, today, by = "day")) +
     xlab("Date") + ylab("") + ggtitle("US - positiveIncrease & hospitalizedCurrently")

US - daily top-3 contributors

If a state has been a top 3 contributor

as_top <- df_states %>%
    filter(date > first_day)%>%
    mutate(str_date = as.character(date))%>%
    group_by(str_date) %>%
    arrange(positiveIncrease, by_group = TRUE)%>%
    slice_tail(n = 3) %>%
    ungroup() %>%
    summarise(unique(state))
as_top <- unlist(as_top)
    
df_states %>%
    filter(state %in% as_top) %>%
    ggplot() +
      stat_steamgraph(mapping = aes(x = date, y = positiveIncrease, group = state, fill = state))  +
      scale_x_date(limits = c(first_day, today), breaks = seq(first_day, today, by = "week"))  +
      scale_y_continuous(breaks = seq(-20000, 20000, by = 5000), labels = c("20000", "15000", "10000", "5000", "0", "5000", "10000", "15000", "20000")) +
      scale_fill_tableau(palette = "Tableau 20") +
      xlab("Date") + ylab("positiveIncrease") + ggtitle("If a state was a top-3 contributor")

US - positiveIncrease by state

num_lag <- 21

find_coef <- function(x, y){
  m <- lm(y ~ x)
  return(coef(m)[2])
}


df_colors <-  df_states %>%
  group_by(state)%>%
  arrange(date, .by_group = TRUE) %>%
  slice_tail(n = num_lag) %>% # last N days
  summarise(trend_coef = find_coef(date, positiveIncrease)) %>% 
  mutate(trend_color = ifelse(trend_coef > 0, "increasing", ifelse(trend_coef < 0, "decreasing", "stable"))) %>% 
  ungroup()%>%
  replace(is.na(.), 0) %>%
  select(state, trend_coef, trend_color) 
 
  
df_states %>% 
    inner_join(df_colors, by = "state") %>%
    ggplot() +
      geom_smooth(mapping = aes(x = date, y = positiveIncrease), color = "gray", alpha = 0.3, method = "loess", size = LINE_SIZE) +
      geom_line(mapping = aes(x = date, y = positiveIncrease, color = trend_color), alpha = 0.7, size = LINE_SIZE) +
      geom_point(mapping = aes(x = date, y = positiveIncrease, color = trend_color), size = 1) +
      scale_x_date(limits = c(first_day, today), breaks = seq(first_day, today, by = "week")) +
      scale_colour_tableau() +
      facet_wrap(state ~ ., ncol = 6, scales = "free") +
      xlab("Date") + ylab("") + ggtitle("US - positiveIncrease by state, colored by the trend of last 21 days")

df_states %>% 
    inner_join(df_colors, by = "state") %>%
    mutate(positiveIncreasePerMillion = positiveIncrease / population * 1000000)%>%
    ggplot() +
      geom_smooth(mapping = aes(x = date, y = positiveIncreasePerMillion), color = "gray", alpha = 0.3, method = "loess", size = LINE_SIZE) +
      geom_line(mapping = aes(x = date, y = positiveIncreasePerMillion, color = trend_color), alpha = 0.7, size = LINE_SIZE) +
      geom_point(mapping = aes(x = date, y = positiveIncreasePerMillion, color = trend_color), size = 1) +
      scale_y_continuous(limits = c(0, 600), breaks = seq(0, 600, by = 150)) +
      scale_x_date(limits = c(first_day, today), breaks = seq(first_day, today, by = "week")) +
      scale_colour_tableau() +
      facet_wrap(state ~ ., ncol = 6, scales = "free")  +
      xlab("Date") + ylab("") + ggtitle("US - positiveIncreasePerMillion by state, colored by the trend of last 21 days")

US - hospitalizedCurrently by state

df_states %>% 
    ggplot() +
      geom_smooth(mapping = aes(x = date, y = hospitalizedCurrently), color = "gray", alpha = 0.3, method = "loess", size = LINE_SIZE) +
      geom_line(mapping = aes(x = date, y = hospitalizedCurrently), alpha = 0.7, color = tableau10[3], size = LINE_SIZE) +
      geom_point(mapping = aes(x = date, y = hospitalizedCurrently), color = tableau10[3], size = 1) +
      scale_x_date(limits = c(first_day, today), breaks = seq(first_day, today, by = "week")) +
      facet_wrap(state ~ ., ncol = 6, scales = "free") +
      xlab("Date") + ylab("") + ggtitle("US - hospitalizedCurrently by state")

US - dailyTestPositiveRate against overallTestedPopulationRate

df_pr <- df_states %>% 
    mutate(testPositiveRate = positiveIncrease / totalTestResultsIncrease, testedPopulationRate = totalTestResults / population) %>%
    filter(testPositiveRate > 0 & testPositiveRate < 1) # rm buggy data to allow log scales
  
df_pr_colors <-  df_pr %>%
  group_by(state)%>%
  arrange(date, .by_group = TRUE) %>%
  slice_tail(n = num_lag) %>% # last N days
  summarise(trend_coef = find_coef(date, testPositiveRate)) %>% 
  mutate(trend_color = ifelse(trend_coef > 0, "increasing", ifelse(trend_coef < 0, "decreasing", "stable"))) %>% 
  ungroup()%>%
  replace(is.na(.), 0) %>%
  select(state, trend_coef, trend_color) 

df_pr %>%
 inner_join(df_pr_colors, by = "state") %>%
 ggplot() +
    geom_smooth(mapping = aes(x = testedPopulationRate, y = testPositiveRate), color = "gray", alpha = 0.3, method = "loess", size = LINE_SIZE) +
    geom_line(mapping = aes(x = testedPopulationRate, y = testPositiveRate, color = trend_color), alpha = 0.7, size = LINE_SIZE) +
    geom_point(mapping = aes(x = testedPopulationRate, y = testPositiveRate, color = trend_color), size = 1) +
    scale_x_continuous(limits = c(0, 0.60), breaks = seq(0, 0.6, by = 0.05)) +
    scale_y_continuous(limits = c(0.001, 1), trans = 'log10', breaks = c(0.001, 0.01, 0.05, 0.1, 0.2, 0.3, 0.5, 0.75, 1)) +
    scale_colour_tableau() +
    facet_wrap(state ~ ., ncol = 6, scales = "free")  +
    xlab("dailyTestPositiveRate") + ylab("overallTestedPopulationRate") + ggtitle("US - dailyTestPositiveRate against overallTestedPopulationRate")

US - death per 10k by state

df_states %>% 
    mutate(deathPer10K = death / population * 10000) %>%
    ggplot() +
     geom_line(mapping = aes(x = date, y = deathPer10K), alpha = 0.7, color = tableau10[3], size = LINE_SIZE) +
     geom_point(mapping = aes(x = date, y = deathPer10K), color = tableau10[3], size = 1) +
     scale_x_date(limits = c(first_day, today), breaks = seq(first_day, today, by = "week")) +
     scale_y_continuous(limits = c(0, 20), breaks = seq(0, 20, by = 5)) +
     facet_wrap(state ~ ., ncol = 6, scales = "free")  +
     xlab("date") + ylab("death per 10k") + ggtitle("US - death per 10k by state")

US - positive per 1k by state

df_states %>% 
    mutate(positivePerOneK = positive / population * 1000) %>%
    ggplot() +
      geom_line(mapping = aes(x = date, y = positivePerOneK), alpha = 0.7, color = tableau10[4], size = LINE_SIZE) +
      geom_point(mapping = aes(x = date, y = positivePerOneK), color = tableau10[4], size = 1) +
      scale_y_continuous(limits = c(0, 25), breaks = seq(0, 25, by = 5)) +
      scale_x_date(limits = c(first_day, today), breaks = seq(first_day, today, by = "week")) +
      facet_wrap(state ~ ., ncol = 6, scales = "free") +
      xlab("date") + ylab("") + ggtitle("US - positivePerOneK by state")

US - tested amount by state

df_states %>% 
    mutate(testResultsIncrease = positiveIncrease + negativeIncrease) %>%
    ggplot() +
      geom_smooth(mapping = aes(x = date, y = testResultsIncrease), color = "gray", alpha = 0.3, method = "loess", size = LINE_SIZE) +
      geom_line(mapping = aes(x = date, y = testResultsIncrease), alpha = 0.7, color = tableau10[7], size = LINE_SIZE) +
      geom_point(mapping = aes(x = date, y = testResultsIncrease), color = tableau10[7], size = 1) +
      scale_x_date(limits = c(first_day, today), breaks = seq(first_day, today, by = "week")) +
      facet_wrap(state ~ ., ncol = 6, scales = "free")  +
      xlab("date") + ylab("testResultsIncrease") + ggtitle("US - testResultsIncrease by state")